https://Carolinah23.github.io

MILESTONE 1 - Analysing data from Water Wells on Baton Rouge, LA

Diana Carolina Hurtado Pulido
October/26/2021

CMPS 3160/6160

Group and work plan I am a graduate student, then I will be working alone for this project.

Description project and Goals

Currently, I am working on finding subsidence rates in Baton Rouge, Louisiana during the last two decades, and investigating what factors are causing subsidence using LiDAR (Light Detection And Ranging) data from 1999 and 2018.

Subsidence is the vertical land movement caused by natural and anthropogenic factors such as sediment compaction, isostatic adjustments, fault slip, and extraction or injection of fluids. The Gulf of Mexico coastline is under constant monitoring due to high rates of Sea Level rise and subsidence, which cause rapid land loss. The study area is not in the coastal area though it is subsiding (Figure 1). This area is of particular interest because there has not been enough research to determine what factors are causing subsidence. This area has two geological faults, grow of urban development, almost 2000 water wells (with different uses) active during the study period, approximately 40 active oil and gas wells, and 11 injection wells.

So far, my results show subsidence in the whole region (Figure 1). Surprisingly subsidence increases from south to north, which is the opposite of the expected results. Areas closer to the coast and water bodies have large subsidence rates due to younger sediments compaction, but most important in this area, the faults move towards the south. Interestingly, small areas show localized subsidence and uplifting, this behavior likely is related to human activities. Then, having these results, the main goal of this project is to find out how these subsidence values are related to groundwater extraction. Important questions:

  1. The location of clusters of wells effectively occurs in areas subsiding or uplifting locally?
  2. Are deeper wells causing more or subsidence than wells at shallow depths?
  3. Is there any particular well use that may be causing more vertical changes?
  4. Using the wells with yield (extraction rate), are high yield values related to subsidence?

Figure 1: Relative subsidence on Baton Rouge using LiDAR differencing between 1999 and 2018. Each map shows different methods applied to find elevation changes (Result of my research)

Data description - Extraction: The data comes from the Department of Natural Resources of Louisiana. The data correspond to the water wells functioning during the period of study (1999 and 2018) in the area. We collected this data (with a collaborator), last year (2020), and calculated the well depth on meters. The data is not complete, the missing values were not published when the well-owners uploaded the information, or the information is too old in some cases.

For my analysis I will use the following variables: Well Depth (on meters), Well Use, Yield (rate of extraction - gallons per minute), and the dates of construction, activation, and deactivation.

Loading data: I and uploading the data using the read_csv()

Tidy data: This dataset has two variables that I will not use: WellDepth and SerialNumber. The first variable will not be used because I will use the information on meters, and the second one is not the main identificator of the observations and is not complete.

There are non numeric values in the Yield variable, and also "NN" values in the LastActive_Plugged_date and DateConstructed that must be changed to NaN using np.nan

The cell below shows the variable types, to datetime the variables Last_Active_Plugged_date and DateConstructed and to number I changed the variables Yeild and Well_Depth_Meters

And finally, I am replace the Y and N (Yes and No) by 1 and 0 in the Active column. This variable indicates if the well was active extracting groundwater to the date that we took the data. For instance, if the well was active between 2001 and 2014 it should has a N, also, if the well is been used to monitior groundwater it should be inactive.